code: def get_anisotropic(n_clusters):
      X, labels = make_blobs(
          n_samples=N_SAMPLES, centers=n_clusters, random_state=170
      )
      transformation = [[0.6, -0.6], [-0.4, 0.8]]
      X = np.dot(X, transformation)
      return X, labels
  
imports: import gradio
  import math
  from functools import partial
  import matplotlib.pyplot
  import numpy
  from sklearn.cluster import AgglomerativeClustering, Birch, DBSCAN, KMeans, MeanShift, OPTICS, SpectralClustering, estimate_bandwidth
  from sklearn.datasets import make_blobs, make_circles, make_moons
  from sklearn.mixture import GaussianMixture
  from sklearn.neighbors import kneighbors_graph
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 6 for calling a global function with example usage: get_anisotropic(n_clusters) and returns: X,  labels